The field of the invention is positron emission tomography (PET) scanners, and particularly PET scanners that can acquire data in a three-dimensional (3D) mode.
Positrons are positively charged electrons which are emitted by radionuclides that have been prepared using a cyclotron or other device. These are employed as radioactive tracers called xe2x80x9cradiopharmaceuticalsxe2x80x9d by incorporating them into substances, such as glucose or carbon dioxide. The radiopharmaceuticals are injected in the patient and become involved in such processes as blood flow, fatty acid, glucose metabolism, and protein synthesis. As the radionuclides decay, they emit positrons. The positrons travel a very short distance before they encounter an electron, and when this occurs, they are annihilated and converted into two photons, or gamma rays. This annihilation is characterized by two features which are pertinent to PET scannersxe2x80x94each gamma ray has an energy of 511 keV and the two gamma rays are directed in nearly opposite directions. An image is created by determining the number of such annihilations at each location within the field of view.
A typical PET scanner is cylindrical and includes a detector ring assembly composed of rings of detectors which encircle the patient and which convert the energy of each 511 keV photon into a flash of light that is sensed by a photomultiplier tube (PMT). Coincidence detection circuits connect to the detectors and record only those photons which are detected simultaneously by detectors located on opposite sides of the patient. The number of such simultaneous events (coincidence events) indicates the number of positron annihilations that occurred along a line joining the two opposing detectors. During an acquisition, coincidence events are recorded to indicate the number of annihilations along lines joining pairs of detectors in the detector ring. These numbers are employed to reconstruct an image using well-known computed tomography techniques.
When originally developed, PET scanners were strictly multiplanar scanners. In such PET scanners, each detector ring is configured to detect annihilations occurring only within the plane of that respective ring alone, or at most within planes defined by detectors on adjacent rings, and not annihilations occurring at other positions within the PET scanner. Because each detector within each detector ring is capable of receiving photons coming in toward the detector from a variety of angles (rather than merely coming in toward the detector from the center of the ring of which the detector is a part), fixed slice septa are positioned in between each of the detector rings of the PET scanners for imaging in what is known as xe2x80x9c2D modexe2x80x9d. The septa, which are commonly composed of lead or tungsten alloy, shield the detectors of each individual detector ring from photons that have not originated from annihilations within the plane defined by the detector ring. The septa further have the function of shielding the detectors of the detector rings from out-of-plane scattered photons or other photons that are not resulting from annihilations (i.e., photons entering at either end of the cylindrical PET scanner).
A major innovation in PET scanners that occurred in the late 1980s and early 1990s has been the development of 3D PET scanners, which include true-3D (or xe2x80x9cvolumetricxe2x80x9d) PET scanners and pseudo-3D PET scanners. In contrast to multiplanar scanners, true-3D PET scanners have no septa and consequently the detectors of each detector ring of the scanners can receive photons from a wider range of angles with respect to the plane of the respective ring than in multiplanar PET scanners. Although pseudo-3D PET scanners do employ septa, the septa are short so as to primarily reduce out of field-of-view (FOV) scatter. 3D PET scanners became feasible partly as a result of the increased speed of computers, since PET imaging in such scanners requires determining the existence of, and processing information related to, coincidence events that occur not merely between pairs of detectors positioned on individual (or adjacent) detector rings, but also between pairs of detectors positioned on different detector rings (or different detector rings that are spaced more than one ring apart from one another). 3D PET scanners allow for increased sensitivity relative to multiplanar scanners, since more true coincidence events can be recorded. However, 3D PET scanners also admit more scattered and random coincidence events to the data set from which the image is reconstructed than multiplanar PET scanners. In particular, scattered coincidence events can account for more than 50% of recorded coincidence events in the case of procedures such as torso imaging.
To address the problem of correcting for scatter in 3D PET scanners, model-based scatter correction methods have been proposed. Model-based scatter correction methods generally involve algorithms that use the acquired PET emission and transmission data to form a set of images, downsample the data to reduce the number of pixels, determine contributed-to detector pairs and calculate the expected flux of single-scatter radiation that is detected in different lines-of-response between different detectors. One such model-based scatter correction method was set forth in an article by John M. Ollinger entitled xe2x80x9cModel-Based Scatter Correction for Fully 3D-PETxe2x80x9d (Phys. Med. Biol. 41, pages 153-176, 1996), which is hereby incorporated by reference herein. Another model-based scatter correction method was set forth in an article by C. C. Watson entitled xe2x80x9cNew, Faster, Image-Based Scatter Correction for 3D-PETxe2x80x9d (IEEE Trans. Nucl. Sci., 44, 90-97, 1997), which also is hereby incorporated by reference herein.
The aforementioned model-based scatter correction methods are pixel (or voxel) driven routines and that use measured data as the inputs. To obtain output results that are based mainly upon true coincidence event data and not scattered coincidence events, the model-based scatter correction methods involve performing multiple iterations of the scatter estimation, where each iteration involves nested looping through several dimensions. For example, the model-based scatter correction method set forth by Ollinger involves processing the data set obtained by the PET scanner by looping through such dimensions as the transaxial distance, the theta angle (angular orientation within a particular detector ring with respect to horizontal) and the azimuthal angle (angular orientation between different detector rings). The model-based scatter correction method set forth by Watson proceeds in a similar manner.
Because of the iterative nature of model -based scatter correction methods, and particularly the nested looping through multiple parameters that is performed according to those methods, the model-based scatter correction methods are limited in their usefulness in conventional 3D PET scanners insofar as the methods employ intensive processing. For example, to perform the first of the above-identified model-based scatter correction methods, nested looping is performed over several parameters, including a first parameter concerning the detector hit by an unscattered photon (d1), a second parameter concerning azimuthal angle, a third parameter concerning in-transverse-plane angle, a fourth parameter concerning the transverse distance across the image pixels, a fifth parameter concerning the transverse distance down the image pixels away from the detector d1 (defined scatter voxel S), and a sixth parameter concerning detectors contributed to for scatter voxel S. Typically, in order to perform three iterations of these nested loops for this model-based scatter correction method using a conventional Sun Ultra-Sparc 360 MHz processor, up to 8-12 minutes of processing time is required.
Because of the continuing need for improvements in the speed and accuracy with which 3D PET images can be produced, it would therefore be advantageous if a method and system was developed in PET scanners that allowed scatter to be corrected in a more rapid, less processing-intensive manner than currently exists.
The present inventor has discovered that it is possible to improve the speed of execution of model-based scatter algorithms by combining axial data within certain ranges into composite transaxial planes or xe2x80x9csuper-slicesxe2x80x9d and thus effectively collapsing data along the axial direction. By so combining the axial data, one can perform the model-based scatter algorithms by looping over the in-plane parameters x and y within each super-slice, instead of looping over both the in-plane parameters and the azimuthal angle dimension. By eliminating the calculations associated with looping over the azimuthal angle dimension, the computation time required for performing the method-based scatter algorithms is reduced.
The present invention relates to a method of processing an image data set of a 3D PET scanner to correct for scattered coincidence events, where the 3D PET scanner includes a detector ring assembly having a central axis. The method of processing the image data set includes providing an image data set including a first plurality of data subsets respectively corresponding to a plurality of native slices. The method additionally includes axially downsampling the first plurality of data subsets to generate a second plurality of data subsets respectively corresponding to a plurality of super-slices. The method further includes calculating scatter in the second plurality of data subsets by way of a model-based scatter correction algorithm.
The present invention additionally relates to a method of processing an image data set of a 3D PET scanner to correct for scattered coincidence events, where the 3D PET scanner includes a detector assembly having a central axis. The method includes (a) providing an emission image data set and transmission image data set, where the emission image data set includes a plurality of emission data subsets respectively corresponding to a plurality of native slices, and where the transmission image data set includes a plurality of transmission data subsets respectively corresponding to the plurality of native slices. The method further includes (b) axially downsampling the plurality of emission data subsets to generate an axially downsampled emission data set corresponding to at least one super-slice, and axially downsampling the plurality of transmission data subsets to generate an axially downsampled transmission data set corresponding to the at least one super-slice. The method additionally includes (c) transaxially downsampling each of the axially downsampled emission data set and the axially downsampled transmission data set to generate a downsampled emission data set and a downsampled transmission data set. The method further includes (d) calculating scatter based upon the downsampled emission and transmission data sets to generate a scatter sinogram data set, and (e) repeating (c)-(d) until scatter has been adequately calculated as determined according to a convergence rule.
The present invention additionally relates to a 3D PET scanner that includes a gantry, a plurality of sets of detectors supported by the gantry, and a processing means. The detectors in each set are disposed in a plane and positioned about a central axis that intersects the plane, and the sets of detectors are spaced along the central axis. Each set of detectors forms respective a native axial slice. The processor means is for providing an image data set including a first plurality of data subsets respectively corresponding to the respective native slices. The processor means additionally is for axially downsampling the first plurality of data subsets to generate a second plurality of data subsets respectively corresponding to a plurality of super-slices. The processor means further is for calculating scatter in the second plurality of data subsets by way of a model-based scatter correction algorithm.